Successor Features for Transfer in Reinforcement Learning (1606.05312v2)

Abstract: Transfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks. We propose a transfer framework for the scenario where the reward function changes between tasks but the environment's dynamics remain the same. Our approach rests on two key ideas: "successor features", a value function representation that decouples the dynamics of the environment from the rewards, and "generalized policy improvement", a generalization of dynamic programming's policy improvement operation that considers a set of policies rather than a single one. Put together, the two ideas lead to an approach that integrates seamlessly within the reinforcement learning framework and allows the free exchange of information across tasks. The proposed method also provides performance guarantees for the transferred policy even before any learning has taken place. We derive two theorems that set our approach in firm theoretical ground and present experiments that show that it successfully promotes transfer in practice, significantly outperforming alternative methods in a sequence of navigation tasks and in the control of a simulated robotic arm.

• The paper presents a novel framework that decouples environment dynamics from rewards using successor features for efficient transfer learning.
• It employs generalized policy improvement to combine multiple policies, enabling rapid adaptation without retraining for new reward structures.
• Experimental results in navigation and robotic control tasks show significant performance gains over traditional methods.

Successor Features for Transfer in Reinforcement Learning

The paper "Successor Features for Transfer in Reinforcement Learning," authored by a team at DeepMind, investigates the application of successor features (SFs) in facilitating transfer learning within reinforcement learning (RL) frameworks. The focus is on enhancing the transfer of learning across tasks by maintaining the same environmental dynamics while allowing changes in reward functions.

Core Concepts

The paper hinges on two central concepts:

1. Successor Features (SFs): This is an extension of Dayan's successor representation, which permits the decomposition of the value function into parts that separate environment dynamics from rewards. This enables a dynamic adjustment to updated reward functions without recalculating the entire policy.
2. Generalized Policy Improvement (GPI): Expanding upon BeLLMan's policy improvement theorem, GPI enables leveraging multiple policies' value functions to devise a superior policy. It ensures that the newly computed policy is at least as good as each of the contributing policies.

Approach and Methodology

The novel RL framework proposed integrates SFs and GPI to create a more efficient transfer learning mechanism by:

• Decoupling environment dynamics and reward via SFs.
• Utilizing GPI to extract the best policy from a collection of previously learned policies.

Theoretical underpinnings are supported by two theorems providing performance guarantees for the transferred policy. They enhance the robustness and applicability of their approach even before any new task-specific learning has been executed.

Experimental Evaluation

The authors demonstrate the efficacy of their approach in two complex environments:

• A navigation task set in a two-dimensional four-room continuous space, where the task objectives varied by adjusting reward values associated with objects.
• A control task involving a simulated robotic arm in a 3D reacher environment, with tasks defined by different target locations.

The results presented illustrate substantial improvements over baseline methods such as Q-learning and prior transfer approaches. Notably, the adoption of SFs and GPI results in superior performance across a range of dynamic tasks.

Implications and Future Directions

The use of SFs and GPI in RL provides a solid groundwork for transfer learning, potentially impacting numerous applications where task environments remain stable but reward mechanisms evolve. The paper's insights may lead to more adaptive and flexible AI systems capable of leveraging past experiences more effectively.

Potential future research may explore optimizing the selection or design of features $\phi$ for SFs to maximize transfer effectiveness further, as well as refining approaches to online learning of transfer relations in continually fluctuating environments.

Conclusion

This research contributes significantly to the field of transfer in reinforcement learning by proposing a sophisticated yet computationally efficient framework that blends SFs and GPI. While it successfully enhances transfer capabilities within RL, it also opens pathways for further exploration into adaptive AI systems capable of dealing with broader classes of tasks and environments.